PASS1A (Rat) Metabolomics:
Mayo Clinic Replicate Comparisons (Pairs)

Setup the Environment

CSS Styling

writeLines("td, th { padding : 3px } th { background-color:white; color:black; border:1px solid black; text-align:center } td {color:black; border:1px solid black; word-wrap:break-word; white-space:nowrap; overflow: hidden; text-overflow: ellipsis; max-width:300px; text-align:left}", con= "../css/style.css")

Comparison of Samples and Metabolites Between Replicate Pairs

• Split data by replicates
• Determine the number of missing values per metabolite
• Determine the number of NA values per metabolite
• Plot boxplots and matched density plot before normalization
• Normalize
• Plot boxplots and matched density plot after normalization

Boxplots (Original Abundances)

# Plot Boxplots faceted by shared metabolites
################################################################################
p <- plot_df %>%
  ggplot(aes(y = METABOLITE_NAME, x = VALUE, color = REPLICATE)) +
  geom_boxplot(alpha = 0.8) +
  labs(title=paste0(tissue,' ',study_institute_metab_family,": Original Metabolite Abundances"),
             x = "Abundance", y = "") 
plot(p)

Zero/Missing Values in Metabolites (Original Abundances)

# Identify Zero/Missing Values in Metabolites
################################################################################
na_df <- plot_df %>%
  ungroup() %>% 
  group_by(METABOLITE_NAME, REPLICATE) %>%
  mutate(ZERO_LOG = ifelse(VALUE == 0, 1, 0)) %>%
  mutate(ZERO_N = sum(ZERO_LOG)) %>%
  mutate(ZERO_FREQ = round(ZERO_N/length(shared_sams), digits = 2)) %>%
  mutate(NA_LOG = ifelse(is.na(VALUE), 1, 0)) %>%
  mutate(NA_N = sum(NA_LOG)) %>%
  mutate(NA_FREQ = round(NA_N/length(shared_sams), digits = 2)) %>%
  select(REPLICATE, METABOLITE_NAME, ZERO_N, ZERO_FREQ, NA_N, NA_FREQ) %>%
  unique() %>%
  arrange(REPLICATE, desc(ZERO_FREQ), desc(NA_FREQ)) %>%
  ungroup()

na_df %>%
  knitr::kable(format = "html") %>%
  scroll_box(width = "100%", height = "400px")

REPLICATE	METABOLITE_NAME	ZERO_N	ZERO_FREQ	NA_N	NA_FREQ
610	Aminoisobutyric acid	78	1.00	0	0
610	5-Hydroxylysine	78	1.00	0	0
610	Homocysteine	72	0.92	0	0
610	3-Methylhistidine	66	0.85	0	0
610	Cystathionine	59	0.76	0	0
610	Carnosine	54	0.69	0	0
610	Anserine	37	0.47	0	0
610	1-Methylhistidine	16	0.21	0	0
610	beta-Alanine	5	0.06	0	0
610	Alanine	0	0.00	0	0
610	Aminoadipic acid	0	0.00	0	0
610	2-Aminobutyric acid	0	0.00	0	0
610	Arginine	0	0.00	0	0
610	Asparagine	0	0.00	0	0
610	Aspartic acid	0	0.00	0	0
610	Citrulline	0	0.00	0	0
610	Cysteine	0	0.00	0	0
610	Ethanolamine	0	0.00	0	0
610	gamma-Aminobutyric acid	0	0.00	0	0
610	Glutamic acid	0	0.00	0	0
610	Glutamine	0	0.00	0	0
610	Glycine	0	0.00	0	0
610	Histidine	0	0.00	0	0
610	Hydroxyproline	0	0.00	0	0
610	Isoleucine	0	0.00	0	0
610	Leucine	0	0.00	0	0
610	Lysine	0	0.00	0	0
610	Methionine	0	0.00	0	0
610	Ornithine	0	0.00	0	0
610	Phenylalanine	0	0.00	0	0
610	Phosphoethanolamine	0	0.00	0	0
610	Proline	0	0.00	0	0
610	Sarcosine	0	0.00	0	0
610	Serine	0	0.00	0	0
610	Taurine	0	0.00	0	0
610	Threonine	0	0.00	0	0
610	Tryptophan	0	0.00	0	0
610	Tyrosine	0	0.00	0	0
610	Valine	0	0.00	0	0
611	Aminoisobutyric acid	78	1.00	0	0
611	5-Hydroxylysine	78	1.00	0	0
611	3-Methylhistidine	67	0.86	0	0
611	Homocysteine	63	0.81	0	0
611	Carnosine	53	0.68	0	0
611	Cystathionine	52	0.67	0	0
611	Anserine	30	0.38	0	0
611	1-Methylhistidine	16	0.21	0	0
611	beta-Alanine	1	0.01	0	0
611	Alanine	0	0.00	0	0
611	Aminoadipic acid	0	0.00	0	0
611	2-Aminobutyric acid	0	0.00	0	0
611	Arginine	0	0.00	0	0
611	Asparagine	0	0.00	0	0
611	Aspartic acid	0	0.00	0	0
611	Citrulline	0	0.00	0	0
611	Cysteine	0	0.00	0	0
611	Ethanolamine	0	0.00	0	0
611	gamma-Aminobutyric acid	0	0.00	0	0
611	Glutamic acid	0	0.00	0	0
611	Glutamine	0	0.00	0	0
611	Glycine	0	0.00	0	0
611	Histidine	0	0.00	0	0
611	Hydroxyproline	0	0.00	0	0
611	Isoleucine	0	0.00	0	0
611	Leucine	0	0.00	0	0
611	Lysine	0	0.00	0	0
611	Methionine	0	0.00	0	0
611	Ornithine	0	0.00	0	0
611	Phenylalanine	0	0.00	0	0
611	Phosphoethanolamine	0	0.00	0	0
611	Proline	0	0.00	0	0
611	Sarcosine	0	0.00	0	0
611	Serine	0	0.00	0	0
611	Taurine	0	0.00	0	0
611	Threonine	0	0.00	0	0
611	Tryptophan	0	0.00	0	0
611	Tyrosine	0	0.00	0	0
611	Valine	0	0.00	0	0

# Remove Metabolites with high NAor Zero Frequencies
################################################################################
met_rm <- na_df %>%
  filter((ZERO_FREQ >= 0.95) | (NA_FREQ >= 0.8)) %>%
  select(METABOLITE_NAME) %>% unlist() %>% unique()
metabolites <- metabolites[metabolites %!in% met_rm]
plot_df <- plot_df %>%
  filter(METABOLITE_NAME %in% metabolites)
plot_df$METABOLITE_NAME <- as.character(plot_df$METABOLITE_NAME)

Distributions (Original Abundances)

# Summary Statistics of Density plot distribution
################################################################################
# Iterate through the runs and collect vectors
df_all <- data.frame()
for(rep in c(reps)){
  # Collect numeric vector
  num_vec <- plot_df %>%
    filter(REPLICATE == rep) %>%
    select(VALUE) %>% unlist() %>% unname()
  df <- NumericSummaryStats(num_vec)
  row.names(df) <- rep
  df_all <- rbind(df_all, df)
}
df_all %>%
  knitr::kable(format = "html") %>%
  scroll_box(width = "100%", height = "200px")

	NA_COUNT	NA_FREQ	MEAN	MEDIAN	MAX	MIN	MID_RANGE	VARIANCE	STD_DEV	SE_MEAN	Q1	Q3	KURTOSIS	SKEW	BC_LAMBDA
611	0	0	3.31	0.75	47.28	0	47.28	50.57	7.11	0.13	0.11	2.35	10.81	3.3	None
610	0	0	3.20	0.72	44.96	0	44.96	47.17	6.87	0.13	0.10	2.36	10.75	3.3	None

# Plot the density plot for all the gene counts
################################################################################
plot_df %>%
  ggplot(aes(x = VALUE, color = REPLICATE)) +
  geom_density() +
  xlim(min(df_all$MIN), mean(df_all$Q3)) +
  labs(title=paste0(tissue,' ',study_institute_metab_family,": Original Metabolite Abundances"),
       x = "Abundance",
       y = "Density")

Metabolite by Metabolite Correlation (Original Abundances)

################################################################################
####################### Metabolite by Metabolite Correlations ##################
################################################################################
# Subset Matrices
x <- plot_df %>%
  unite(METABOLITE_NAME,REPLICATE, 
        col = METABOLITE_NAME_REPLICATE, remove = F) %>%
  select(METABOLITE_NAME_REPLICATE, labelid, VALUE,REPLICATE) %>%
  split(.$REPLICATE) %>%
  map(select, -REPLICATE) %>%
  map(pivot_wider, names_from = METABOLITE_NAME_REPLICATE, values_from = VALUE) %>%
  map(arrange, labelid) %>%
  map(tibble::column_to_rownames, var = "labelid") %>%
  map(as.matrix)

# Subset matrices
if(all(row.names(x[[1]]) == row.names(x[[2]]))){
  shared_met_mat <-  do.call(cbind,x) 
}

Corr <- 'Spearman'
# Create an NxN Matrix of correlation
if(Corr == 'Pearson'){
        cor1 <- stats::cor(shared_met_mat, method = 'pearson') %>% round(digits = 3)
}else if(Corr == 'Spearman'){
        cor1 <- stats::cor(shared_met_mat, method = 'spearman') %>% round(digits = 3)
}


# Reorder the correlation matrix
cormat <- cor1

# Melt the correlation matrix
melted_cormat <- melt(cormat, na.rm = TRUE)

################################################################################
####################### Only Reciprocal Pairs ##################################
################################################################################

# Remove rows that compare metabolites from the same dataset, them remove redundent measurements
melted_cormat2 <- melted_cormat 
# Widen the datafrmae back to matrix
cormat2 <- melted_cormat2 %>%
  pivot_wider(names_from = Var2, values_from = value) %>%
  as.data.frame()
row.names(cormat2) <- cormat2$Var1
cormat2 <- cormat2 %>%
  select(-Var1) %>%
  as.matrix()
cormat2 <- cormat2[sort(rownames(cormat2)),sort(colnames(cormat2))]
cormat2 <- cormat2[grepl(reps[1], rownames(cormat2)),grepl(reps[2], colnames(cormat2))]

# Orient the colors and breaks
paletteLength <- 1000
myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength)
# length(breaks) == length(paletteLength) + 1
# use floor and ceiling to deal with even/odd length pallettelengths
myBreaks <- c(seq(-1, 0, length.out=ceiling(paletteLength/2) + 1), 
              seq(1/paletteLength, max(cor1, na.rm = T), length.out=floor(paletteLength/2)))

# Plot the heatmap
pheatmap(cormat2, color=myColor, breaks=myBreaks, 
                  cluster_rows = F, cluster_cols = F,fontsize = 6)

Scatterplots (Original Abundances)

# Subset the data for sites to compare (scatterplots)
###################################################
plot_join_df <- countdata_df %>%
  ungroup() %>%
  unite(STUDY_INSTITUTE,METAB_FAMILY, col = "STUDY_INSTITUTE_METAB_FAMILY") %>%
  filter(TISSUE == tissue) %>%
  filter(NAMED == named) %>%
  filter(STUDY_INSTITUTE_METAB_FAMILY == study_institute_metab_family) %>%
  unnest(COUNT_DATA) %>%
  filter(viallabel %in% as.character(unlist(sample_join$sample_id))) %>%
  filter(METABOLITE_NAME %in% metabolites) %>%
  unite(labelid, viallabel, col = "labelid_viallabel", remove = F) %>%
  mutate(REPLICATE = case_when(grepl(paste0(reps[1],"$"),labelid_viallabel) ~ reps[1],
                              grepl(paste0(reps[2],"$"),labelid_viallabel) ~ reps[2],
                              )) %>%
  select(REPLICATE,METABOLITE_NAME,labelid,VALUE) %>%
  mutate(REPLICATE = as.character(REPLICATE)) %>%
  split(.$REPLICATE) %>%
  map(~rename(., !!sym(unique(.$REPLICATE)) := "VALUE")) %>%
  map(~select(., -REPLICATE)) %>%
  purrr::reduce(left_join, by = c("labelid","METABOLITE_NAME"))

# Plot scatter plots
################################################################################
# Plot scatter plots ordered by sample (include R2 values)
lm_df <- plot_join_df %>%
  rename(name = METABOLITE_NAME) %>%
  rename(x = reps[1]) %>%
  rename(y = reps[2])
# Iterate through each of the shared metabolites to collect summary info about their correlations
r2_df <- data.frame()
for(metab in metabolites){
  met_df <- lm_df %>%
    filter(name == metab)
  r2_df <- rbind(r2_df, lm_eqn(met_df))
}
# Display summaries
met_r2_df <- r2_df %>%
  arrange(METABOLITE) %>%
  select(METABOLITE,R2) %>%
  arrange(desc(R2))
met_r2_df %>%
  knitr::kable(format = "html") %>%
  scroll_box(width = "100%", height = "200px")

METABOLITE	R2
2-Aminobutyric acid	0.92
Sarcosine	0.768
Hydroxyproline	0.435
Alanine	0.364
Aspartic acid	0.352
Tryptophan	0.261
Glutamic acid	0.238
Ornithine	0.182
Tyrosine	0.163
Anserine	0.112
Threonine	0.0956
Asparagine	0.0919
Citrulline	0.0892
Cystathionine	0.0874
Glutamine	0.0848
Valine	0.0716
Aminoadipic acid	0.0571
beta-Alanine	0.0485
Isoleucine	0.0442
3-Methylhistidine	0.0423
Phosphoethanolamine	0.0387
Proline	0.0321
1-Methylhistidine	0.0293
Carnosine	0.028
Lysine	0.0205
gamma-Aminobutyric acid	0.0201
Ethanolamine	0.018
Methionine	0.0138
Homocysteine	0.00987
Glycine	0.00965
Leucine	0.00811
Phenylalanine	0.0058
Taurine	0.00521
Serine	0.00448
Cysteine	0.00444
Arginine	0.00249
Histidine	0.000709

# Collect the order of metabolites
scat_met_order <- met_r2_df %>% select(METABOLITE) %>% unlist() %>% as.character()

# Plot the scatter plots
for(metab in scat_met_order){
  p <- plot_join_df %>%
    filter(METABOLITE_NAME == metab) %>%
  ggplot(aes(x = !!sym(reps[1]), y = !!sym(reps[2])), color = ) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm", size = 1) +
  geom_abline(linetype="dashed") +
  facet_wrap(~ METABOLITE_NAME) +
  expand_limits(x = 0, y = 0) +
  #labs(title=paste0(tissue,' ',study_institute_metab_family,": Normalized Metabolite Abundances")) +
  coord_fixed(ratio=1)
  plot(p)
}

Sample by Sample Correlations (Original Abundances)

################################################################################
####################### Sample by Sample Correlations ##########################
################################################################################

# Subset Data
################################################################################
# Original NxP Matrix
x <- plot_df %>%
  unite(labelid,REPLICATE, 
        col = labelid_REPLICATE, remove = F) %>%
  select(labelid_REPLICATE, METABOLITE_NAME, VALUE,REPLICATE) %>%
  split(.$REPLICATE) %>%
  map(select, -REPLICATE) %>%
  map(pivot_wider, names_from = METABOLITE_NAME, values_from = VALUE) %>%
  map(tibble::column_to_rownames, var = "labelid_REPLICATE") %>%
  map(as.matrix)
# Subset matrices
shared_sam_mat <-  do.call(rbind,x) %>% 
  t()

# Create an NxN Matrix of correlation
Corr <- 'Spearman'
if(Corr == 'Pearson'){
        cor1 <- stats::cor(shared_sam_mat, method = 'pearson') %>% round(digits = 3)
}else if(Corr == 'Spearman'){
        cor1 <- stats::cor(shared_sam_mat, method = 'spearman') %>% round(digits = 3)
}

# Melt the correlation matrix
melted_cormat <- melt(cor1, na.rm = TRUE)

################################################################################
####################### Unclustered ############################################
################################################################################

# Remove rows that compare metabolites from the same dataset, them remove redundent measurements
melted_cormat2 <- melted_cormat

# Widen the dataframwe back to matrix
cormat2 <- melted_cormat2 %>%
  pivot_wider(names_from = Var2, values_from = value) %>%
  as.data.frame()
row.names(cormat2) <- cormat2$Var1
cormat2 <- cormat2 %>%
  select(-Var1) %>%
  as.matrix()
cormat2 <- cormat2[sort(rownames(cormat2)),sort(colnames(cormat2))]
cormat2 <- cormat2[grepl(reps[1], rownames(cormat2)),grepl(reps[2], colnames(cormat2))]

# Orient the colors and breaks
paletteLength <- 1000
myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength)
# length(breaks) == length(paletteLength) + 1
# use floor and ceiling to deal with even/odd length pallettelengths
myBreaks <- c(seq(-1, 0, length.out=ceiling(paletteLength/2) + 1), 
              seq(1/paletteLength, max(cor1, na.rm = TRUE), length.out=floor(paletteLength/2)))

# Plot the heatmap
pheatmap(as.matrix(cormat2), color=myColor, breaks=myBreaks, 
                  cluster_rows = F, cluster_cols = F,fontsize = 6)

PCA Analysis (Original Abundances)

pca <- prcomp(na.omit(t(shared_sam_mat)), scale. = F)
percentVar <- pca$sdev^2/sum(pca$sdev^2)
PC <- pca$x %>% as.data.frame()
PC$labelid_rep <- as.character(row.names(pca$x))
PC <- PC %>%
  tidyr::separate(col = labelid_rep, into = c('labelid', 'REPLICATE'),
  sep = "_", remove = T)
# Join the phenotype data
PC <- left_join(PC, pheno_df, by = 'labelid')

p0 <- PC %>%
  ggplot(aes(x = PC1, y = PC2, color=REPLICATE)) +
  geom_point(size = 3) +
  geom_line(aes(group = labelid),color="dark grey") +
  ggtitle('Shared Samples and Shared Metabolites') + 
  xlab(paste0('PC1: ',round(percentVar[1]*100,2),'% variance')) +
  ylab(paste0('PC2: ',round(percentVar[2]*100,2),'% variance')) +
  theme(aspect.ratio=1)
p0

p1 <- PC %>%
        ggplot(aes(x = PC1, y = PC2, color=Key.anirandgroup, shape = REPLICATE)) +
        geom_point(size = 3) +
  geom_line(aes(group = labelid),color="dark grey") +
        ggtitle('Shared Samples and Shared Metabolites') + 
        xlab(paste0('PC1: ',round(percentVar[1]*100,2),'% variance')) +
        ylab(paste0('PC2: ',round(percentVar[2]*100,2),'% variance')) +
        scale_color_manual(values=ec_colors) +
        theme(aspect.ratio=1)
p1

PC$Registration.sex <- as.character(PC$Registration.sex)
p2 <- PC %>%
        ggplot(aes(x = PC1, y = PC2, color=Registration.sex, shape = REPLICATE)) +
        geom_point(size = 3) +
  geom_line(aes(group = labelid),color="dark grey") +
        ggtitle('Shared Samples and Shared Metabolites') + 
        xlab(paste0('PC1: ',round(percentVar[1]*100,2),'% variance')) +
        ylab(paste0('PC2: ',round(percentVar[2]*100,2),'% variance')) +
        theme(aspect.ratio=1)
p2

Normalization (AutoScale)

# Normalize
# Note in this step, we use labelid to account for mayo's duplicate samples
################################################################################
norm_df <- plot_df %>%
  unite(labelid, viallabel, col = "labelid_viallabel", remove = F) %>%
  split(.$REPLICATE) %>%
  map(select, labelid_viallabel, METABOLITE_NAME, VALUE) %>%
  map(pivot_wider, names_from = METABOLITE_NAME, values_from = VALUE) %>%
  map(column_to_rownames, var = "labelid_viallabel") %>%
  map(as.matrix) %>%
  map(AutoScaleMatrix) %>%
  map(as.data.frame) %>%
  map(rownames_to_column, var = "labelid_viallabel") %>%
  map(pivot_longer, names_to = 'METABOLITE_NAME', values_to = 'VALUE', cols = all_of(metabolites)) %>%
  map(mutate, REPLICATE = case_when(grepl(paste0(reps[1],"$"),labelid_viallabel) ~ reps[1],
                                     grepl(paste0(reps[2],"$"),labelid_viallabel) ~ reps[2],
                                    grepl(paste0(reps[3],"$"),labelid_viallabel) ~ reps[3])) %>%
  bind_rows()

Boxplots (Normalized Abundances)

# Plot Boxplots faceted by shared metabolites
################################################################################
norm_df %>%
  ggplot(aes(y = METABOLITE_NAME, x = VALUE, color = REPLICATE)) +
  geom_boxplot(alpha = 0.8) +
  labs(title=paste0(tissue,' ',study_institute_metab_family,": Normalized Metabolite Abundances"),
             x = "Abundance", y = "") 

Metabolite by Metabolite Correlation (Normalized Abundances)

################################################################################
####################### Metabolite by Metabolite Correlations ##################
################################################################################
# Subset Matrices
x <- plot_df %>%
  unite(METABOLITE_NAME,REPLICATE, 
        col = METABOLITE_NAME_REPLICATE, remove = F) %>%
  select(METABOLITE_NAME_REPLICATE, labelid, VALUE,REPLICATE) %>%
  split(.$REPLICATE) %>%
  map(select, -REPLICATE) %>%
  map(pivot_wider, names_from = METABOLITE_NAME_REPLICATE, values_from = VALUE) %>%
  map(arrange, labelid) %>%
  map(tibble::column_to_rownames, var = "labelid") %>%
  map(as.matrix) %>%
  map(AutoScaleMatrix)

# Subset matrices
if(all(row.names(x[[1]]) == row.names(x[[2]]))){
  shared_met_mat <-  do.call(cbind,x) 
}

Corr <- 'Spearman'
# Create an NxN Matrix of correlation
if(Corr == 'Pearson'){
        cor1 <- stats::cor(shared_met_mat, method = 'pearson') %>% round(digits = 3)
}else if(Corr == 'Spearman'){
        cor1 <- stats::cor(shared_met_mat, method = 'spearman') %>% round(digits = 3)
}

# Reorder the correlation matrix
cormat <- cor1

# Melt the correlation matrix
melted_cormat <- melt(cormat, na.rm = TRUE)

################################################################################
####################### Only Reciprocal Pairs ##################################
################################################################################

# Remove rows that compare metabolites from the same dataset, them remove redundent measurements
melted_cormat2 <- melted_cormat 
# Widen the datafrmae back to matrix
cormat2 <- melted_cormat2 %>%
  pivot_wider(names_from = Var2, values_from = value) %>%
  as.data.frame()
row.names(cormat2) <- cormat2$Var1
cormat2 <- cormat2 %>%
  select(-Var1) %>%
  as.matrix()
cormat2 <- cormat2[sort(rownames(cormat2)),sort(colnames(cormat2))]
cormat2 <- cormat2[grepl(reps[1], rownames(cormat2)),grepl(reps[2], colnames(cormat2))]

# Orient the colors and breaks
paletteLength <- 1000
myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength)
# length(breaks) == length(paletteLength) + 1
# use floor and ceiling to deal with even/odd length pallettelengths
myBreaks <- c(seq(-1, 0, length.out=ceiling(paletteLength/2) + 1), 
              seq(1/paletteLength, max(cor1, na.rm = T), length.out=floor(paletteLength/2)))

# Plot the heatmap
pheatmap(cormat2, color=myColor, breaks=myBreaks, 
                  cluster_rows = F, cluster_cols = F,fontsize = 6)

Scatterplots (Normalized Abundances)

# Normalize
# Subset the data for sites to compare (scatterplots)
###################################################
norm_join_df <- countdata_df %>%
  ungroup() %>%
  unite(STUDY_INSTITUTE,METAB_FAMILY, col = "STUDY_INSTITUTE_METAB_FAMILY") %>%
  filter(TISSUE == tissue) %>%
  filter(NAMED == named) %>%
  filter(STUDY_INSTITUTE_METAB_FAMILY == study_institute_metab_family) %>%
  unnest(COUNT_DATA) %>%
  filter(viallabel %in% as.character(unlist(sample_join$sample_id))) %>%
  unite(labelid, viallabel, col = "labelid_viallabel", remove = F) %>%
  mutate(REPLICATE = case_when(grepl(paste0(reps[1],"$"),labelid_viallabel) ~ reps[1],
                                     grepl(paste0(reps[2],"$"),labelid_viallabel) ~ reps[2],
                               grepl(paste0(reps[3],"$"),labelid_viallabel) ~ reps[3])) %>%
  split(.$REPLICATE) %>%
  map(select, labelid_viallabel, METABOLITE_NAME, VALUE) %>%
  map(pivot_wider, names_from = METABOLITE_NAME, values_from = VALUE) %>%
  map(column_to_rownames, var = "labelid_viallabel") %>%
  map(as.matrix) %>%
  map(AutoScaleMatrix) %>%
  map(as.data.frame) %>%
  map(rownames_to_column, var = "labelid_viallabel") %>%
  map(pivot_longer, names_to = 'METABOLITE_NAME', values_to = 'VALUE', cols = all_of(metabolites)) %>%
  map(mutate, REPLICATE = case_when(grepl(paste0(reps[1],"$"),labelid_viallabel) ~ reps[1],
                                     grepl(paste0(reps[2],"$"),labelid_viallabel) ~ reps[2],
                                    grepl(paste0(reps[3],"$"),labelid_viallabel) ~ reps[3])) %>%
  map(~rename(., !!sym(unique(.$REPLICATE)) := "VALUE")) %>%
  map(~select(., -REPLICATE)) %>%
  map(mutate, labelid = gsub(pattern = "_..*", replacement = '', labelid_viallabel)) %>%
  map(select, -labelid_viallabel) %>%
  purrr::reduce(left_join, by = c("labelid","METABOLITE_NAME"))

# Plot scatter plots ordered by sample (include R2 values)
lm_df <- norm_join_df %>%
  rename(name = METABOLITE_NAME) %>%
  rename(x = reps[1]) %>%
  rename(y = reps[2])
# Iterate through each of the shared metabolites to collect summary info about their correlations
r2_df <- data.frame()
for(metab in metabolites){
  met_df <- lm_df %>%
    filter(name == metab)
  r2_df <- rbind(r2_df, lm_eqn(met_df))
}
# Display summaries
met_r2_df <- r2_df %>%
  arrange(METABOLITE) %>%
  select(METABOLITE,R2) %>%
  arrange(desc(R2))
met_r2_df %>%
  knitr::kable(format = "html") %>%
  scroll_box(width = "100%", height = "200px")

METABOLITE	R2
2-Aminobutyric acid	0.92
Sarcosine	0.768
Hydroxyproline	0.435
Alanine	0.364
Aspartic acid	0.352
Tryptophan	0.261
Glutamic acid	0.238
Ornithine	0.182
Tyrosine	0.163
Anserine	0.112
Threonine	0.0956
Asparagine	0.0919
Citrulline	0.0892
Cystathionine	0.0874
Glutamine	0.0848
Valine	0.0716
Aminoadipic acid	0.0571
beta-Alanine	0.0485
Isoleucine	0.0442
3-Methylhistidine	0.0423
Phosphoethanolamine	0.0387
Proline	0.0321
1-Methylhistidine	0.0293
Carnosine	0.028
Lysine	0.0205
gamma-Aminobutyric acid	0.0201
Ethanolamine	0.018
Methionine	0.0138
Homocysteine	0.00987
Glycine	0.00965
Leucine	0.00811
Phenylalanine	0.0058
Taurine	0.00521
Serine	0.00448
Cysteine	0.00444
Arginine	0.00249
Histidine	0.000709

# Collect the order of metabolites
scat_met_order <- met_r2_df %>% select(METABOLITE) %>% unlist() %>% as.character()

# Plot scatter plots (Normalized)
################################################################################
# Plot the scatter plots
for(metab in scat_met_order){
  p <- norm_join_df %>%
    filter(METABOLITE_NAME == metab) %>%
  ggplot(aes(x = !!sym(reps[1]), y = !!sym(reps[2])), color = ) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm", size = 1) +
  geom_abline(linetype="dashed") +
  facet_wrap(~ METABOLITE_NAME) +
  expand_limits(x = 0, y = 0) +
  #labs(title=paste0(tissue,' ',study_institute_metab_family,": Normalized Metabolite Abundances")) +
  coord_fixed(ratio=1)
  plot(p)
}

# norm_join_df %>%
#   mutate(METABOLITE_NAME = factor(METABOLITE_NAME, levels = scat_met_order)) %>%
#   ggplot(aes(x = !!sym(reps[1]), y = !!sym(reps[2])), color = ) +
#   geom_point(alpha = 0.5) +
#   geom_smooth(method = "lm", size = 1) +
#   geom_abline(linetype="dashed") +
#   facet_wrap(~ METABOLITE_NAME) +
#   expand_limits(x = 0, y = 0) +
#   labs(title=paste0(tissue,' ',study_institute_metab_family,": Normalized Metabolite Abundances")) +
#   coord_fixed(ratio=1)

Distributions (Normalized Abundances)

# Plot the density plot for all the gene counts
################################################################################
norm_df %>%
  ggplot(aes(x = VALUE, color = REPLICATE)) +
  geom_density() +
  labs(title=paste0(tissue,' ',study_institute_metab_family,": Normalized Metabolite Abundances"),
       x = "Abundance",
       y = "Density")

# Summary Statistics of Density plot distribution
################################################################################
# Iterate through the runs and collect vectors
df_all <- data.frame()
for(rep in reps){
  # Collect numeric vector
  num_vec <- norm_df %>%
    filter(REPLICATE == rep) %>%
    select(VALUE) %>% unlist() %>% unname()
  df <- NumericSummaryStats(num_vec)
  row.names(df) <- rep
  df_all <- rbind(df_all, df)
}
df_all
#}

Sample by Sample Correlations (Normalized Abundances)

################################################################################
####################### Sample by Sample Correlations ##########################
################################################################################

# Subset Data
################################################################################
# Original NxP Matrix
x <- plot_df %>%
  unite(labelid,REPLICATE, 
        col = labelid_REPLICATE, remove = F) %>%
  select(labelid_REPLICATE, METABOLITE_NAME, VALUE,REPLICATE) %>%
  split(.$REPLICATE) %>%
  map(select, -REPLICATE) %>%
  map(pivot_wider, names_from = METABOLITE_NAME, values_from = VALUE) %>%
  map(tibble::column_to_rownames, var = "labelid_REPLICATE") %>%
  map(as.matrix) %>%
  map(AutoScaleMatrix)
# Subset matrices
shared_sam_mat <-  do.call(rbind,x) %>% 
  t()

# Create an NxN Matrix of correlation
Corr <- 'Spearman'
if(Corr == 'Pearson'){
        cor1 <- stats::cor(shared_sam_mat, method = 'pearson') %>% round(digits = 3)
}else if(Corr == 'Spearman'){
        cor1 <- stats::cor(shared_sam_mat, method = 'spearman') %>% round(digits = 3)
}

# Melt the correlation matrix
melted_cormat <- melt(cor1, na.rm = TRUE)

################################################################################
####################### Unclustered ############################################
################################################################################

# Remove rows that compare metabolites from the same dataset, them remove redundent measurements
melted_cormat2 <- melted_cormat

# Widen the dataframwe back to matrix
cormat2 <- melted_cormat2 %>%
  pivot_wider(names_from = Var2, values_from = value) %>%
  as.data.frame()
row.names(cormat2) <- cormat2$Var1
cormat2 <- cormat2 %>%
  select(-Var1) %>%
  as.matrix()
cormat2 <- cormat2[sort(rownames(cormat2)),sort(colnames(cormat2))]
cormat2 <- cormat2[grepl(reps[1], rownames(cormat2)),grepl(reps[2], colnames(cormat2))]

# Orient the colors and breaks
paletteLength <- 1000
myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength)
# length(breaks) == length(paletteLength) + 1
# use floor and ceiling to deal with even/odd length pallettelengths
myBreaks <- c(seq(-1, 0, length.out=ceiling(paletteLength/2) + 1), 
              seq(1/paletteLength, max(cor1, na.rm = TRUE), length.out=floor(paletteLength/2)))

# Plot the heatmap
pheatmap(as.matrix(cormat2), color=myColor, breaks=myBreaks, 
                  cluster_rows = F, cluster_cols = F,fontsize = 6)

PCA Analysis (Normalized Abundances)

# Plot a PCA with outliers labeled
# shared_sam_mat <- shared_sam_mat %>%
#   t() %>% AutoScaleMatrix() %>%
#   t()
pca <- prcomp(na.omit(t(shared_sam_mat)), scale. = F)
percentVar <- pca$sdev^2/sum(pca$sdev^2)
PC <- pca$x %>% as.data.frame()
PC$labelid_rep <- as.character(row.names(pca$x))
PC <- PC %>%
  tidyr::separate(col = labelid_rep, into = c('labelid', 'REPLICATE'),
  sep = "_", remove = T)
# Join the phenotype data
PC <- left_join(PC, pheno_df, by = 'labelid')

p0 <- PC %>%
  ggplot(aes(x = PC1, y = PC2, color=REPLICATE)) +
  geom_point(size = 3) +
  geom_line(aes(group = labelid),color="dark grey") +
  ggtitle('Shared Samples and Shared Metabolites') + 
  xlab(paste0('PC1: ',round(percentVar[1]*100,2),'% variance')) +
  ylab(paste0('PC2: ',round(percentVar[2]*100,2),'% variance')) +
  theme(aspect.ratio=1)
p0

p1 <- PC %>%
        ggplot(aes(x = PC1, y = PC2, color=Key.anirandgroup, shape = REPLICATE)) +
        geom_point(size = 3) +
  geom_line(aes(group = labelid),color="dark grey") +
        ggtitle('Shared Samples and Shared Metabolites') + 
        xlab(paste0('PC1: ',round(percentVar[1]*100,2),'% variance')) +
        ylab(paste0('PC2: ',round(percentVar[2]*100,2),'% variance')) +
        scale_color_manual(values=ec_colors) +
        theme(aspect.ratio=1)
p1

PC$Registration.sex <- as.character(PC$Registration.sex)
p2 <- PC %>%
        ggplot(aes(x = PC1, y = PC2, color=Registration.sex, shape = REPLICATE)) +
        geom_point(size = 3) +
  geom_line(aes(group = labelid),color="dark grey") +
        ggtitle('Shared Samples and Shared Metabolites') + 
        xlab(paste0('PC1: ',round(percentVar[1]*100,2),'% variance')) +
        ylab(paste0('PC2: ',round(percentVar[2]*100,2),'% variance')) +
        theme(aspect.ratio=1)
p2